Method for Transmitting Information to Device on a Network Based upon Information
Associated with a Different Device on the Network

ABSTRACT

A method to transmit information to an internet-connected device based upon information associated with a different internet-connected device, said method comprising: collecting observations over the internet, each observation including an internet-connected device identity, a server source/destination identifier, and a timestamp indicating a time of occurrence of the network connection; identifying multiple potential internet-connected device pairings between internet-connected devices from the observations, based at least in part upon scoring of the observations association score for the pairing; and selecting, for the at least one selected internet-connected device pairing, information to send to one internet-connected device identified by the at least one selected internet-connected device pairing based at least in part on information associated with another internet-connected device identified by the selected at least one internet-connected device pairing.

RELATED APPLICATIONS

The present Nonprovisional U.S. Patent Application claims the benefit ofthe previous U.S. Provisional Patent Application entitled “System andMethod for Determining Related Digital Identities” filed with the U.S.Patent Office on May 10,2012 and having Ser. No. 61/645,549.

TECHNICAL FIELD

The present indention relates to the field of digital identities anddigital advertising. In particular, but not by way of limitation, thepresent disclosure teaches techniques for determining related digitalidentities.

BACKGROUND

The internet has changed the mass media landscape forever. Before theinternet became a mainstream mass media system, advertisers weregenerally limited to communicating with potential customers usingtelevision, radio, and print media (newspapers and magazines)advertising. With the popularization of the global internet, advertiserscan now advertise to billions of computer users as those computer usersbrowse the World Wide Web on the internet.

Internet advertising has become a very large industry. Two of the mostcommonly used advertising channels on the World Wide Web are internetsearch advertising and banner advertisements. Internet searchadvertising operates by allowing users to enter search keywords into aninternet search service and then Interspersing advertisements (generallyrelated to the search keywords) within the results of the internetsearch. Banner advertisements are defined areas of a web page thatcontain advertisements in the same manner that traditional magazines andnewspapers use newsprint area for advertising. Both internet searchresult advertisements and internet banner-advertisements have asignificant advantage over prior advertising systems since the recipientof an internet advertisement may click on the internet advertisement toobtain more information or directly proceed to an Internet retailer foran immediate sale.

The internet advertising industry for advertising on personal computersystems on the internet has matured and become very sophisticated. Theinternet advertisers and internet advertising services use severaltechniques of obtaining information about internet users such that themost appropriate advertisements may be selected for each internet user.For example, Internet advertisement services may track the web browsinghistory from particular personal computer to determine the interests ofthat user and thus create a demographic profile of that internet user.Furthermore, the contents of a web page that is being delivered topersonal computer may be analyzed to help select an appropriate banneradvertisement that is closely related to the contents of the web page.

Although internet advertising to personal computer users that arebrowsing the World Wide Web has become relatively sophisticated, theoverall internet advertising industry is still in its infancy. There arenow many new digital electronic devices that use the internet and can beused to deliver advertising to their user. For example, cellulartelephones, video game consoles, set-top video streaming boxes, internetradio devices, and tablet computer systems can all be used to deliverinternet advertisements to their respective users. The techniques usedto select and deliver advertisements to the users of these emerginginternet platforms are relatively primitive. Thus, it would be desirableto provide tools that provide an improved ability to select appropriateinternet advertisements to users of these new internet-connected digitalelectronic devices.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which arc not necessarily drawn to scale, like numeralsdescribe substantially similar components throughout the several views.Like numerals having different letter suffixes represent differentinstances of substantially similar components. The drawings illustrategenerally, by way of example, but not by way of limitation, variousembodiments discussed in the present document.

FIG. 1 illustrates a diagrammatic representation of machine in theexample form of a computer system within which a set of instructions,for causing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed.

FIG. 2A conceptually illustrates a household with several digitaldevices that access the internet which are potential digital identitypairs.

FIG. 2B conceptually illustrates the digital devices of three differentpeople used at four different locations: household A, household B, aworkplace W, and a cyber café.

FIG. 3 illustrates a high-level flow diagram that describes oneembodiment of a digital identity pairing system.

FIG. 4 illustrates an example set of observation samples of the variousdigital identities (C_(X), D_(X), C_(Y), D_(Y), C_(Z), D_(Z)) at fourdifferent source locations (household A, household B, workplace W, and acyber café) depleted in FIG. 2B.

FIG. 5A graphically illustrates an example of a series of dailyassociation scores for a particular potential digital identity paircalculated over time.

FIG. 5B graphically illustrates the daily association scores of FIG. 5Acollected in a set of association score range buckets to identify astatistical mode.

FIG. 6 illustrates the observation data from FIG. 4 but in Boolean formfor use in calculating a Boolean system association score.

FIG. 7 illustrates a flow diagram describing how a digital pairingsystem may use a weighted score of frequency count association scoresand Boolean system association scores to identify high-probabilitydigital identity pairs.

FIG. 8 illustrates a set of destination usage observations for fourdifferent digital devices illustrated in FIG. 2B.

FIG. 9A conceptually illustrates a household with two users that eachuse desktop computer, a laptop computer, and a cellular smartphone.

FIG. 9B conceptually illustrates website usage patterns for thecomputers in FIG 9A and inferred website usage patterns for the cellularsmartphones in FIG. 9A.

FIG. 10A. illustrates a log of usage triads wherein each triad containsa client identifier, destination, and an associated timestamp.

FIG. 10B illustrates a set of sessions for the laptop computers C_(X)and C_(Y) in household A of FIG. 2B.

FIG. 11 graphically illustrates all of the observed gap times are placedinto gap time buckets as illustrated in

DETAILED DESCRIPTION

The following detailed description includes references to theaccompanying drawings, which form a part of the detailed description.The drawings show illustrations in accordance with example embodiments.These embodiments, which are also referred to herein as “examples,” aredescribed in enough detail to enable those skilled in the art topractice the invention. It will be apparent to one skilled in the artthat specific details in the example embodiments are not required inorder to practice the present invention. For example, although some ofthe embodiments are mainly disclosed with reference to cellulartelephones, the techniques disclosed in this document may be used withother types of digital electronic devices such as tablet computersystems and video game systems. The example embodiments may be combined,other embodiments may be utilized, or structural, logical and electricalchanges may be made without departing from the scope of what is claimed.The following detailed description is, therefore, not to be taken in alimiting sense, and the scope is defined by the appended claims andtheir equivalents.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one. In this document, the term“or” is used to refer to a nonexclusive or, such that “A or B” includes“A but not B,” “B but not A,” and “A and B,” unless otherwise indicated.Furthermore, all publications, patents, and patent documents referred toin this document are incorporated by reference herein in their entirety,as though individually incorporated by references. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

Computer Systems

The present disclosure concerns digital computer systems. FIG. 1illustrates a diagrammatic representation of a machine in the exampleform of a computer system 100 that may be used to implement portions ofthe present disclosure. Within computer system 100 of FIG. 1, there area set of instructions 124 that may be executed for causing the machineto perform any one or more of the methodologies discussed within thisdocument.

In a networked, deployment, the machine of FIG. 1 may operate in thecapacity of a server machine or a client machine in a client-servernetwork environment, or as a peer machine in a peer-to-peer (ordistributed) network environment. The machine may be a personal computer(PC), a tablet computer, a set-top box (STB), a Personal DigitalAssistant (PDA), a cellular telephone, a web appliance, a server, anetwork router, a network switch, a network bridge, a video gameconsole, or any machine capable of executing a set of computerinstructions (sequential or otherwise) that specify actions to be takenby that machine. Furthermore, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 100 of FIG. 1 includes a processor 102(e.g., a central processing unit (CPU), a graphics processing unit (GPU)or both), a main memory 104, and a non volatile memory 106, whichcommunicate with each other via a bus 108. The non volatile memory 106may comprise flash memory and may be used either as computer systemmemory, as a file storage unit, or both. Both the main memory 104 and anon volatile memory 106 may store instructions 124 and data 125 that areprocessed by the processor 102.

The computer system 100 may include a video display adapter 110 thatdrives a video display system 115 such as a Liquid Crystal Display (LCD)in order to display visual output to a user. The computer system 100 mayalso include other output systems such as signal generation device 118that drives an audio speaker.

Computer system 100 includes a user input system 112 for accepting inputfrom a human user. The user input system 112 may include an alphanumericinput device such as a keyboard, a cursor control device (e.g., a mouseor trackball), touch sensitive pad (that may be overlaid on top of videodisplay 115), a microphone, or any other device for accepting input froma human user.

The computer system 100 may include a disk drive unit 116 for storingdata. The disk drive unit 116 includes a machine-readable medium 122 onwhich is stored one or more sets of computer instructions and datastructures (e.g., instructions 124 also known as ‘software’) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. The instructions 124 may also reside, completely or atleast partially, within the main memory 104 and/or within a cache memory103 associated with the processor 102. The main memory 104 and thenon-volatile memory 106 associated with, the processor 102 alsoconstitute machine-readable media. The non-volatile memory 106 maycomprise a removable flash memory device.

The computer system 100 may include one more network interface devices120 for transmitting and receiving data on one or more networks 126. Forexample wired or wireless network interfaces 120 may couple to a localarea network 126. Similarly, a cellular telephone network interface 120may be used to couple to a cellular telephone network 126. The variousdifferent networks 126 are often coupled directly or indirectly to theglobal internet 101. The instructions 124 and data 125 used by computersystem 100 may be transmitted or received over network 126 via thenetwork interface device 120. Such transmissions may occur utilizing anyone of a number of well-known transfer protocols such as the well knownFile Transport Protocol (FTP).

Note that not all of the parts illustrated within FIG. 1 will be presentin all embodiments. For example, a computer server system may not have avideo display adapter 110 or video display system 115 if that server iscontrolled through the network interface device 120. Similarly, a tabletcomputer or cellular telephone will generally not have a disk drive unit116 and instead use flash memory or another form of long-term storage.

While the machine-readable medium 122 is shown in an example embodimentto be a single medium, the term “machine-readable medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of instructions. The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring, encoding or carrying a set of infractions for execution by themachine and that cause the machine to perform any one or more of themethodologies described herein, or that is capable of storing, encodingor carrying data structures utilized by or associated with such a set ofinstructions. The term “machine-readable medium” shall accordingly betaken to include, hut not be limited to, solid-state memories, opticalmedia, battery-backed RAM, and magnetic media.

For the purposes of this specification, the term “module” includes anidentifiable portion of code, computational or executable instructions,data, or computational object to achieve a particular function,operation, processing, or procedure. A module need not be implemented insoftware; a module may be implemented in software, hardware/circuitry,or a combination of software and hardware.

Internet Advertising

The global internet has become a mass medium that connects publishers ofinformation with consumers of information. Some of the publishing on theinternet is done on a subscription or paid-for basis wherein a consumerpays for access to specific information. For example, a news publishermay create news web site that provides specific premium content only tocustomers that pay a subscription fee. However, a very large portion ofthe informational content available on the global internet is freelyavailable. Web site content, videos, podcasts, and games are all freelyavailable on the internet. To fund much of the freely available contenton the global internet, internet publishers rely on advertisers that payto have their advertisements displayed alongside and embedded within aninternet publisher's content.

Although internet advertising started with advertisements to personalcomputer users browsing the World Wide Web with web browser programs,the internet advertising market has grown significantly beyond thatearly stage. FIG. 2A conceptually illustrates a household A 250 that iscoupled to the global internet 201 through an internet connection 267.Household A 250 uses a wireless network access point 265 to provideinternet access to many different digital electronic devices withinhousehold A 250. An internet user may use a laptop computer 251 or 261for traditional personal computer based browsing of internet web sitesthat are supported by internet advertising. However, all of the otherinternet-connected devices in household A 250 may also be used todisplay internet advertisements. Cellular telephones 252 and 262 maydisplay banner advertisements in web pages displayed by a micro browseror within advertisement-supported application programs such as gamesdownloaded onto the cellular phones. Tablet computer system 257 may alsodisplay internet advertisements in a web browser or in other applicationprograms that are supported by advertising. A video game console 259 maydisplay advertisements within a navigator application or even within thevideo games that users play on the video game console 259.

Thus, as illustrated in FIG. 2A, many different digital electronicdevices within household A 250 may be served advertisements frominternet advertisers. In the future, many new internet-connected digitaldevices will be introduced and each new internet-connected digitaldevice may be used to display internet advertisements to its user. Notethat all of the internet-connected digital devices within household A250 will generally share a single internet Protocol (IP) address 263that is assigned, to the Wi-Fi router device 265 that couples householdA 250 to the global internet 201.

In order to really provide substantial value to internet advertisers,internet advertising must be well targeted. Advertising suntan lotionduring the winter to a person living in Minnesota does not provide muchvalue to the suntan lotion manufacturer. Thus, internet advertisingservices attempt to learn as much as possible about their audience inorder to select the most appropriate internet advertisements. As setforth in the background, internet web site publishers that advertise tousers running web browsers on personal computers have developed manydifferent techniques for learning about their users. These techniquesmay include:

-   -   Generating user profiles based on web browsing histories    -   Using search terms entered in to search engines to target        advertisements    -   Using the content of web pages to select matching advertisements    -   Creating user registration systems where users divulge        demographic data    -   Using information on the PC to help select advertisements

However, these advertising targeting techniques developed for personalcomputer based internet advertising often cannot be used with otherinternet-connected digital devices. For example, there may be no webbrowsing history on the video game console 259 of household A 250. Eventhough the cellular telephones 252 and 262 and the tablet computersystem 257 may have web browsing histories to draw from, the users mayonly rarely use those devices for web browsing such that their verylimited browsing history does not provide an accurate demographicprofile of the user to guide advertisement selection. It is thereforeoften much more difficult to provide well-targeted advertising tointernet-connected digital devices other than personal computer systems.

Digital Identify Pairing Overview

Referring to FIG. 2A, the people that live at household A 250 useseveral mobile internet-connected digital devices to communicate withthe internet 201. Specifically, the laptop computer systems (251 and261), the cellular phones (252 and 262), and the tablet computer system257 are all mobile internet-connected digital devices. These mobiledevices can be and often are taken with the user to other locations thatprovide wireless (or wired) internet access. For example, user X in thehousehold may use laptop computer system 251 and cellular phone 252 bothat household 250 and at a workplace W 210.

When user X brings laptop computer system 251 and cellular phone 252 toworkplace W 210 those two devices obviously can no longer use Wi-Firouter 265 to access the internet 201. Instead, user X connects laptopcomputer system 251 to the local area network 229 at work and configurescellular phone 252 to use a local Wi-Fi network provided by wirelessaccess point 225. With these two internal connections, user X will thenbe able to access the internet 201 using laptop computer system 251 andcellular phone 252 through the firewall/proxy server 221 at theworkplace 210.

When user X is at home A 250, the laptop computer system 251 and thecellular phone 252 will both use a single IP address A 263 that is onWi-Fi router 265. Similarly, when that same user X is at workplace 210,laptop computer system 251 and cellular phone 252 will both use thesingle IP address W 223 that is on firewall/proxy server 221. Given thatspecific internet usage pattern data, an astute observer that knowsnothing about user X could make the rational inference that laptopcomputer system 251 and cellular phone 252 are very likely used by thesame person since laptop computer system 251 and cellular phone 252 areused together at both household 250 and at workplace 210. After havingmade such an inference, an advertiser may link together a digitalidentifier associated with laptop computer system 251 and a digitalidentifier associated with cellular phone 252 for advertising purposes.Such a pairing of distinct digital identities to a single user isreferred to as digital identity pairing.

Assuming that a digital identity pairing has been performed accurately(the two paired digital devices are actually used by the same person)then this digital identity pairing may be used to greatly improve thetargeting of internet advertising to both of the linked platforms. Forexample, if an advertising service correctly deduces that laptopcomputer system 251 and cellular phone 252 are used by the same personthen the advertiser can advertise cellular telephone accessories for thespecific brand of cellular phone 252 when that person uses their laptopcomputer system 251 to browse the World Wide Web. More importantly, whenthat person (user X) uses an advertising supported application oncellular phone 252, advertisers can leverage the longer and moredetailed user profile data that has been collected from laptop computersystem 251 to accurately select targeted advertisements for display onthat cellular phone 252. Thus, digital identity pairing can greatlyimprove the quality of internet advertising on digital devices that havebeen correctly identified as belonging to the same user.

Digital Identity Pairing Basics

Referring to FIG. 2A, there many different internet-connected devices(251, 252, 261, 262, 257, and 259) at household A 250 that may accessthe internet 201. All of these internet-connected devices at household A250 will use the same IP address A 263 that is on Wi-Fi router 265.Similarly, when the user of laptop computer system 251 and cellularphone 252 is at workplace 210 those two devices will have the same IPaddress W 223 as all of the other internet connected devices (211, 212,213, 214, 215, and 225) that share the connection to the interact 201through firewall/proxy device 221. Therefore, the task of specificallyidentifying laptop computer system 251 and cellular phone 252 as a pairof related digital devices used by the same human user is a difficulttask. To make the association, sophisticated analysis is performed onthe server logs of internet web site servers and internet advertisementservers that collect information on every internet-connected device thatmakes any type of request to the server.

FIG. 3 illustrates a high-level flow diagram that describes how adigital identity pairing system may operate in one embodiment.Initially, at stage 310, internet usage data is collected for manydifferent digital identities. In one embodiment, the basic internetusage data that is collected for analysis are data triads consisting ofa client identifier, a common source/destination identifier, and atimestamp. Each part of the data triad will be individually described.

The client identifier is used to identify a specific client device, aclient program, a user identity, or any other type of digital identity.Examples of client identifiers include web browser cookies, cellulartelephone device identifiers, MAC addresses, userIds, and any othersimilar identifier that is linked to a specific client device, clientprogram, or user. The teachings of the present disclosure may be usedwith a wide variety of different client identifiers. In example digitalidentity pairings that will be disclosed with reference to FIGS. 2A and2B, web browser cookies on laptop computer systems and deviceindentifiers on cellular phone devices are used as client identifiers.However, the disclosed techniques may be used with any other suitableclient identifiers that can be used to identify specific client devices,web browsers, users, or other digital identities.

The common source/destination identifier is the identity of some sourceor destination that client devices (as identified by their clientidentifiers) will likely have in common if the two client devices arerelated. In the situation depicted in FIG. 2A, the Internet Protocol(IP) addresses are the common source/destination identifier that may beused to link related client devices. Specifically, the digital identitypairing system may use the fact that laptop computer system 251 andcellular phone 252 both share the IP address W 223 when at work and theIP address A 263 when at home to deduce that laptop computer system 251and cellular phone 252 are related client devices.

The timestamps in each data triad may be used to ensure that the dataused is relevant. The ownership of internet connected devices may changeover time such that very old internet usage data should not be used.Furthermore, many Internet Protocol addresses are “dynamic addresses”that may be used by different entities at different times. Thus,internet usage data observations should have relatively close temporalrelations in order to provide accurate digital identity pairing results.In addition to ensuring that internet usage observations are temporallyproximate, certain embodiments of the disclosed system use thetimestamps of internet usage data triads in a more sophisticated manneras will be disclosed in a later section of this document.

The triads of internet usage data (client identifier, commonsource/destination identifier, and timestamp) may be collected byinternet servers that track each internet server request received. Inparticular, internet advertisement services and internet web publishersthat track each advertisement or web page served are excellent sourcesof internet usage data information. Individual application programs(such as games, media aggregators, utilities, etc.) that run on clientdevices and report usage information to servers on the internet are alsoexcellent sources of usage data.

Referring back to FIG. 3, after collecting internet usage data the nextstep in digital identity pairing is to determine a set of potentialdigital identity pairs. There are many millions of different digitalidentities involved in internet activities every day. Attempting toanalyze every possible permutation of digital identities as a potentialdigital identity pair would be an extremely difficult and probablyfutile task. Thus, to reduce the size of the digital identity pairingproblem, the gathered internet usage data is analyzed at stage 320 toidentify a much smaller number of potential digital identity pairs thathave a decent probability of being related.

In embodiments that use IP addresses as common source/destinationidentifiers, two different techniques have been used to select potentialdigital identity pairs for further analysis. A first strategy is toexamine the number of different digital identities known to use the sameIP address. Specifically, if less than a threshold number of digitalidentities are known to use a specific IP address then all of thedifferent logical pairings of digital identities from that single IPaddress may be viewed as potential digital identity pairs. The reasoningis that if there are just a few different digital identities related toa single common IP then there is a good probability that some of thosedifferent digital identities are associated with the same person andthat one may be able to statistically link the digital identitiesbelonging to that same person. For example, a family household thatshares a single internet account will likely have family members thatuse more than one digital identity that can be statistically linked.

In one embodiment the threshold value is set to six such that if thereare six or less digital identities seen at a particular IP address thenvarious logical combinations of those six or less digital identities maybe considered potential digital identity pairs. For example, in FIG. 2Ahousehold A 250 has only six different digital devices (251, 252, 261,262, 257, and 259) that couple to the internet 201 though a single IPaddress A 263 on Wi-Fi router 265 such that the various digital devicesin household A 250 may be considered as potential digital identitypairs. In contrast, a very large number of digital devices couple to theinternet 201 though the single IP address W 223 at workplace W 210 suchthat the digital identity pairing system does not immediately considerall of the combinations of digital devices at workplace 219 as potentialdigital identity pairs. In effect, the system identifies familyhouseholds (which often have less than six internet connected devices)and then attempts to pair up digital devices from the family householdthat are used by the same user.

In another embodiment, the digital identity pairing system considers thespecific IP address origin and determines if that IP address is anaddress where paired digital identities are likely to be found (such ashousehold residences as set forth above). All of the static IP addresseson the internet are allocated by the internet Corporation for AssignedNames and Numbers (ICANN). By examining who owns a particular IPaddress, one may make a determination as to whether it will be easy toidentify related digital identities may be located at that IP address.Thus, for example, IP addresses that are used by an internet serviceprovider (ISP) to provide residential internet service may be good IPaddresses to use when identifying potential digital identity pairs.Various other systems of identifying residential household IP addressesmay also be used. In addition, other techniques of identifying likelydigital identity pairs may also be used in step 330 in addition to orinstead of the systems for identifying residential households.

After selecting sets of potential digital identity palm at stage 330,the digital identity pairing system then processes the gathered internetusage data at stage 340 to determine association scores for thepotential digital identity pairings. Those digital identity pairingswith the most favorable association scores will be deemed most likely tobe associated with the same human user. Many different techniques may beused to calculate association scores for the potential digital identitypairings. A detailed explanation of one particular method of calculatingassociation scores (and other related metrics that may also be used) ispresented in a later section of this document but various otherdifferent scoring systems may be used.

Since the observed internet usage data will vary over time and certainchance activities may cause false digital identity associations to bedetected, thee association scores may be post-processed to remove noise.For example, association scores may be smoothed out over time usingvarious techniques. Thus, at stage 360, the association score data thathas been generated over time may be post-processed such that outlierdata points are largely filtered out. The end result of stage 360 is aset of high probability digital identity pairings.

Finally, at stage 390, the identified high probability digital identitypairs are used to improve the targeting of internet advertising to bothof the paired digital identities. The accumulated profile data from thetwo separate digital identities may be combined in a synergistic mannerto provide a detailed profile of the human user associated with thepaired digital identities. This detailed profile may then be used toselect the best internet advertisements for that human user when eitherof the digital identities requires an internet advertisement.

A Basic Digital Identity Pairing Method

As set forth in the flow diagram of FIG. 3, one embodiment of thedigital identity pairing system first identifies pairs of digitalidentities that have a good probability of being a related pair (instage 330) and then processes the collected internet usage data forthose two digital identities to generate an ‘association score’ for thepotential pair (in stage 340). The calculated association score may beused to determine if the two digital identities are deemed to beassociated with the same human user. To describe how association scoresare calculated in one specific embodiment, a detailed example is herebyset forth with reference to FIG. 2B and FIG. 4.

FIG. 2B illustrates the household A 250 and the workplace W 210 of FIG.2A with an additional household B 280 and a cyber café C 290. Inhousehold A 250 there are two digital device users: User X and User Y.User X regular uses laptop computer system 251 and cellular phone 252such that the digital identities of laptop computer system 251 andcellular phone 252 should be identified as paired digital identities.User X's laptop computer system 251 is digitally identified using acookie (C) on a web browser such that it is labelled C_(X) and user X'scellular phone 252 is digitally identified with a deviceID (D) such thatit is labelled D_(X). This digital device labelling nomenclature will beused with the other laptop and cellular phones as well. User Y ofhousehold A 250 regularly uses laptop computer system C_(Y) 261 andcellular phone D_(Y) 262 such that C_(Y) 261 and D_(Y) 262 should alsobe identified as a related pair of digital identities. Recall that allof the digital devices (C_(X) 251, D_(X) 252, C_(Y) 261, and D_(Y) 262)in household A 250 will use the same IP address A 263 that is assignedto Wi-Fi router 265 when those digital devices are used at household A250. Note that although this embodiment is based open using web browsercookies arid mobile device identifiers, any other similar identifiersthat can be associated with the digital devices may be used.

User Z resides at household B 280 regularly uses laptop computer systemC_(Z) 281 and cellular phone D_(Z) 282. While at household B 280, bothC_(Z) 281 and D_(Z) 282 will use IP address B 283 that is assigned toWi-Fi router 285 in use at household B 280. Both user X and user Z worktogether at workplace W 210 such that C_(X) 251, D_(X) 252, C_(Z) 281,and D_(Z) 282 are regularly used at workplace W 210. While at workplaceW 210 those digital devices will all use IP address W 223 that isassigned to firewall/proxy 221 at workplace W 210. Many other digitaldevices (211, 212, 213, 214, 215, and 216) will also use IP address W223 at workplace W 210.

Finally, FIG. 2B also illustrates a cyber café 290 that offers freeWi-Fi service to customers of cyber café 290. User Z and user Y frequentcyber café C 290 such that C_(Z) 281, D_(Z) 282, and D_(Y) 262 areillustrated at cyber café 290 where IP address C 293 is used on Wi-Firouter 295. Note that many other visitors (not shown) will also frequentcyber café 290. However, the various digital devices only seen togetherat cyber café 290 will not be considered potential digital identitypairs since there are too many digital identity pairings seen togetherat cyber café 290.

After collecting internet usage data (as set forth in stage 310), thenext step in identifying digital identity pairs is to select a set ofpotential digital identity pairs as set forth in stage 320 of FIG. 3. Asset forth in the previous section, the various combinations of digitalidentities that are associated with IP addresses having six or lessdigital identities may be selected as potential digital identitypairings. In the example of FIG. 2B, the various combinations of thedigital devices at households A 250 and B 280 are therefore consideredpotential digital identity pairs, (The workplace W 210 and cyber café C290 have too many different digital identities associated with them andthus do not provide good candidates for digital identity pairs.) Forsimplicity, only the possible digital identity pairings (C_(X), D_(X)),(C_(X), D_(Y)), (C_(Y), D_(X)) and (C_(Y), D_(Y)) from household A 210will be analyzed in this example.

After identifying a set of potential digital identity pairs, the digitalidentity pairing system then calculates association scores for all ofthe potential digital identity pairs as set forth in stage 340 of FIG.3. In one embodiment, the pairing system attempts to pair each digitaldevice with only one web browser cookie. (However, more than onedifferent digital device may be paired with the same browser cookie.)Thus, among two competing cookies attempting to be paired with the samedigital device, the digital identity pairing with the higher associationscore may be selected. For example, between potential pairings (C_(X),D_(X)) and (C_(Y), D_(X)), the pair with the higher association scoremay be selected as the most likely digital identity pair. Similarly,between potential digital identity pairings (C_(X), D_(Y)) and (C_(Y),D_(Y)) the pair with the higher association score may be selected.

In one particular embodiment, the digital identity pairing system uses avariation of Bayesian probability analysis to calculate an associationscore tor each of the potential cookie and deviceID digital identitypairs. In addition a “support” score and “confidence” score may also becalculated. The support, confidence, and association scores may bedefined as follows:

Support=P(cookie, deviceID)

Confidence=P(cookie|deviceID)

Association(cookie→deviceID)=P(cookie|deviceID)/P(cookie)

These three scores may be used to Identify digital identity pairings andto rate the confidence in a digital identity pairing that has been made.The support score gives an indication of how much data support there isfor the analysis of this particular cookie and deviceID pair. Theconfidence score gives an indication of how much confidence there is inthe association score. The association score provide rating of howclosely the cookie and deviceID are associated.

In the present disclosure, the support, confidence, and associationscores are calculated using the set of internet usage observations onthe various digital identities being considered that were collected instage 310 of FIG. 3. The following equations describe how the internetusage observations may be used to calculate the support, confidence, andassociation scores.

co-occurrences(cookie, deviceID)=number of times both cookie anddeviceID at the same location (same source identifier IP address).

P(cookie, deviceID)co-occurrences(cookie, deviceID)/total sample size

P(cookie|deviceID)=co-occurrences(cookie,deviceID)/occurrences(deviceID)

P(cookie)=number of occurrence (cookie)/total sample size

To best illustrate the manner in which support, confidence, andassociation scores may be calculated in one embodiment, the associationscores for some potential digital identity pairings in FIG. 2B will becalculated using a set of digital identity internet usage observationsdepicted in FIG. 4. The internet usage observation samples of FIG. 4 area set of observations of the various digital identities (C_(X), D_(X),C_(Y), D_(Y), C_(Z), D_(Z)) at various different source locations(household A, household B, workplace W, and the cyber café C) that areall within a specified time window to keep the data fresh. Theseinternet usage observations come from collected internet usage datatriads (client identifier, common source/destination identifier, andtimestamp) wherein the client identifiers are the PC browser cookies andcellular phone deviceIDs, the common source/destination identifiers arethe IP address of the location where a client identifier was observed,and the timestamps are all within a defined window.

To illustrate how the digital identity pairing system works, an exampleis hereby presented wherein the digital identity pairing system attemptsto pair cellular phone D_(X) 252 from household A 250 with one of thelaptop computers C_(X) 251 or C_(Y) 261 used at household A 250. Tocalculate an association score for D_(X) and C_(Y) the followinginformation from the table in FIG. 4 is needed:

-   -   1) C_(Y) was observed 2 times at home A    -   2) D_(X) was observed 4 times at home A and 8 times at workplace        W    -   3) C_(Y) and D_(X) co-occurred 2 times at home A    -   4) There are 90 total observations

The above observations are then used to calculateAssociation(C_(Y)→D_(X)) as follows:

P(C _(Y))=# of occurrence (C _(Y))/total sample size=(2+7)/97=11/97

P(C _(Y) |D _(X))=co-occurrences(C _(Y) , D _(X))/occurrences(D_(X)=2/(4+8)=1/6

Association(C _(Y) →D _(X))=P(C _(Y) |D _(X))/P(C_(Y))=(1/6)/(11/97)=1.47

The other potential pairing for cellular phone D_(X) 252 is the pairingof cellular phone D_(X) 252 with laptop computers C_(X) 251. Tocalculate an association score for the pair of C_(X) and D_(X) thefollowing information from the table in FIG. 4 is needed:

-   -   1) C_(X) was observed 3 times at home A and 10 times at        workplace W    -   2) D_(X) was observed 4 times at home A and 8 times at workplace        W    -   3) C_(X) and D_(X) co-occurred 3 times at home A and 8 times at        workplace W    -   4) There are 97 total observations

The above observations are then used to calculateAssociation(C_(X)→D_(X)) as follows:

P(C _(X))=# of occurrence (C _(X))/total sample size=(3+10)/90=13/97

P(C _(X) |D _(X))=co-occurrences(C _(X) , D _(X))/occurrences(D_(X)=(3+8)/(4+8)=11/12

Association(C _(X) →D _(X))=P(C _(X) |D _(X))/P(C_(X))=(11/12)/(13/97)=6.34

When comparing the two association scores, the higher association scoreis selected. In this case, the Association(C_(X)→D_(X))=6.34 score ismuch higher than the Association(C_(Y)→D_(X))=1.47 score such that thepairing of laptop computer system C_(X) 251 and cellular phone D_(X) 252are deemed to be a high-probability digital ideality pair. The supportand confidence scores for this pairing are as follows:

Support=P(C _(X) , D _(X))=co-occurrences(C _(X) , D _(X))/total samplesize

Support=(3+8)/97=11/97

Confidence=P(C _(X) |D _(X))=co-occurrences(C _(X) , D_(X))/occurrences(D _(X))

Confidence=(3+8)/(4+8)=11/12

The Support metric may he used to filter out observations that do nothave enough statistical significance. In one embodiment the minimumSupport metric is calibrated based on a desired Precision/Recallmeasurement score. The Confidence metric is a value calculated as partof the Association score.

In household B 280, the only possible digital identity pairing is oflaptop computer system C_(Z) 281 and cellular phone D_(Z) 282. Tocalculate an association score for C_(Z) and D_(Z) the followinginformation from the table in FIG. 4 is needed:

-   -   1) C_(Z) observations: 5 at home B, 20 at workplace W, and 4 at        café C.    -   2) D_(Z) observations: 8 at home B, 10 at workplace W, and 4 at        café C.    -   3) C_(Z) & D_(Z) co-occurrence: 5 at home B, 10 at workplace W,        and 4 at café.    -   4) There are 9 total observations

The above observations are then used to calculateAssociation(C_(Z)→D_(Z)) as follows:

P(C_(Z)) = #  of  occurrence  (C_(X))/total  sample  size = (5 + 20 + 4)/97 = 29/97$\mspace{20mu} \begin{matrix}{P\left( {{C_{Z}D_{Z}} = {{co}\text{-}{{{occurrences}\left( {C_{Z},D_{Z}} \right)}/{{occurrences}\left( D_{Z} \right)}}}} \right.} \\{= {{\left( {5 + 10 + 4} \right)/\left( {8 + 10 + 4} \right)} = {19/22}}}\end{matrix}$Association(C_(Z) → D_(Z)) = P(C_(Z)D_(Z))/P(C_(Z)) = (19/22)/(29/97) = 2.89  Support = P(C_(Z), D_(Z)) = co-occurrences(C_(Z), D_(Z))/total  sample  size  Support = (5 + 10 + 4)/97 = 19/97Confidence = P(C_(Z)D_(Z)) = co-occurrences(C_(Z), D_(Z))/occurrences(D_(Z))  Confidence = (5 + 10 + 4)/(8 + 10 + 4) = 19/22

Since C_(Z) and D_(Z) is the only possible digital identity pairing ofdigital identities at household B 280 there are no other associationscores to directly compare it against. Thus, some threshold value may beused to determine whether that association score is large enough todetermine that the two devices should be paired. The 19/97 support scoreand 19/22 confidence score may also be used to help determine if C_(Z)and D_(Z) will be considered as a digital identity pair.

Post-Processing Association Scores

The association scores calculated for a potential digital identity pairwill vary over time depending on the specific digital identity usageobservations that are being used. To eliminate this ‘noise’ in the data,various techniques may be used to smooth out the data and provide moreconsistent results. Thus, as set forth in stage 360 of FIG. 3, a digitalidentity pairing system may post-process association scores to improvethe overall accuracy of the system.

One simple means of post-processing association scores to improve theresults is to discard association scores that fall below a particulardesignated threshold level. Association scores that fall below adesignated threshold level may simply be irrelevant noise.

FIG. 5A graphically illustrates an example of a series of dailyassociation scores for a particular potential digital identity paircalculated over time. Each association score is actually calculatedusing a moving window of recent digital identity usage observations asconceptually illustrated in the lower left of the graph. In the exampleof FIG. 5A, each association score is calculated using digital identityusage observations from the last five days. Using a moving windowfilters out some of variability in the data but the data is stillsomewhat variable as illustrated in FIG. 5A. Note that a five-day movingwindow is just an arbitrary selection and the digital identity pairingsystem may user other time scales. Selection of the data window sizewill be made by measuring the precision and recall of different sizesand selecting the window size that provides the best result.

To further reduce the noise in the association score data, a digitalidentity pairing system may post-process association scores to eliminatesome of the outlying data samples. For example, if a person goes on avacation then that person's digital device usage pattern may varydramatically and thus eliminate a digital identity pair that wasdiscovered. Proper post-processing of the association scores may preventa temporary change in usage patterns from eliminating an accurately madedigital identity pairing.

In one embodiment, the digital identity pairing system collects a numberof association scores and calculates a statistical mode of theassociation scores that have been gathered over time. The statisticalmodes of different association scores are then compared against eachother to determine the high-probability pairs (instead of comparingrecently calculated association scores directly). Using statisticalmodes may effectively reduce the noise in the sampled digital identityusage data.

One method of calculating a statistical mode involves first creating aset of different association score range buckets. The width of theassociation score range buckets will vary depending on the data density.Then, the collected set of association scores (such as the scores fromFIG. 5A) are divided into the various association scores buckets therebycreating a count of the number of association scores that fall withineach association score range bucket. An example of this is conceptuallyillustrated in FIG. 5B. The association score range bucket with thegreatest number of association scores in that bucket is deemed to be thestatistical mode in one embodiment and may be used to compare againstother association score statistical modes calculated in the same manner.

Various other post-processing methods may also be used to smooth out theassociation scores. For example, instead of using a statistical mode,other implementations may use a median, a mean, or another method ofsmoothing out the association scores.

Boolean Association Score System

As set forth in the previous sections, one embodiment of digitalidentity pairing system operates by counting the frequency ofobservations of a digital identity (such as a cookie or a deviceidentifier) at a particular source/destination address (such as an IPaddress). The internet usage frequency are then used to identify a setof potential digital identity pairs that are then processed to determinea set of association scores for each of the potential digital identitypairs.

The internet usage frequency data can be heavily biased by the number oftimes that a digital identity is linked to a particularsource/destination identifier. For example, a user that spends a verylarge amount of time using a laptop computer system at work but onlyrarely uses that laptop computer system while at home will have resultsthat are heavily biased by the large number of data samples collectedfrom the user's work location. To reduce this heavy biasing that mayoccur; some embodiments employ a Boolean based association score systemthat does not operate by counting the number of times (frequency) adigital identity is observed at a source/destination in a specified timeperiod. Instead, the Boolean association score system only countswhether a particular digital identity was observed or not at asource/destination identifier during the relevant time period.

FIG. 6 illustrates the observed usage data from FIG. 4 but in Booleanform instead of frequency count form. Thus, if a particular digitalidentity was observed at a particular location during a specified timeperiod then a ‘1’ is assigned and when there is no observation a “0” isassigned. Once the digital identity observation data has been convertedto Boolean form, the Boolean observation data can be processed intoBoolean-based association scores using slightly modified techniques. Oneimportant difference is that instead of using the total number ofobservations as a sample size, the number of possible source/destinationlocation identifiers is considered as the sample size. Therefore, thefollowing equation has changed:

P(cookie)=number of occurrences(cookie)/possible locations

To fully explain the Boolean association score calculation methodology,as example is presented with reference to the Boolean usage datadisclosed in FIG. 6. Specifically, to calculate a Boolean systemassociation score of C_(X) and D_(Y) the following information from thetable in FIG. 6 is used:

-   -   1) C_(X) was observed at home A and at workplace W    -   2) D_(Y) was observed at home A and at the café.    -   3) C_(X) and D_(Y) co-occurred at home A    -   4) There are 4 places where observations were made.

The above observations arc then used to calculateAssociation(C_(X)→D_(Y)) as follows:

P(C _(X))=# of occurrence (C _(X))/total sample size=(1+1)/4=1/2

P(C _(X) |D _(Y))=co-occurrences(C _(X) , D _(Y))/occurrences(D_(Y))=1/2

Association(C _(X) →D _(Y))=P(C _(X) |D _(Y))/P(C _(X))=(1/2)/(1/2)=1

The other potential pairing for C_(X) is the pairing of C_(X) and D_(X).To calculate a Boolean system association score of C_(X) and D_(X) thefollowing information from the table in FIG. 6 is needed:

-   -   1) C_(X) was observed at home A and at workplace W    -   2) D_(X) was observed at home A and at workplace W    -   3) C_(X) and D_(X) co-occurred at home A and at workplace W    -   4) There arc 4 places where observations were made.

The above observations, are then used to calculateAssociation(C_(X)→D_(X)) as follows:

P(C _(X))=# of occurrence (C _(X))/total sample size=(1+1)/4=1/2

P(C _(X) |D _(X))=co-occurrences(C _(X) , D _(X))/occurrences(D_(X))=(1+1)/(2)=1

Association(C _(X) →D _(X))=P(C _(X) |D _(X))/P(C _(X))=(1)/(1/2)=2

As with the frequency count based system, when comparing the two Booleansystem association scores, the higher association score may be selected.In this case, the Association(C_(X)→D_(X))=2 score is higher than theAssociation(C_(X)→D_(Y))=1 score such that the pairing of laptopcomputer system (C_(X)) 251 and cellular phone (D_(X)) 252 are deemed tobe a high-probability digital identity pair (not the pairing C_(X) andD_(Y)).

Combined Frequency and Boolean Counting Score

Both the frequency counting system and the Boolean counting system havetheir own advantages. To benefit from the advantages in both approaches,some embodiments of the digital pairing system employ a weighted averageof the frequency counted association score and the Boolean countedassociation score. FIG. 7 illustrates a flow diagram describing how sucha two score based digital identity pairing system may operate. Note thatalthough FIG. 7 and this section describes a system that uses twodifferent score, other embodiments may consider 3, 4, or more differenttypes of association scores.

Initially, at stage 710, the digital identity internet usage data iscollected in the same manner as previously described. Next, at stage720, the digital identity internet usage data is analyzed to select aset of potential digital identity pairings that will further beanalyzed. After this point, the internet usage data is then analyzedwith the two different scoring systems: a frequency counting system anda Boolean counting system.

Along a first path on the left side of FIG. 7, the digital identityinternet usage data is analyzed with a frequency counting based scoringsystem. At stage 740, the internet usage data is processed withfrequency counting to generate a first set of association scores. Onepossible method of implementing a frequency counting system forcalculating association scores is described in the earlier “A BasicDigital Identity Pairing Method” section of this document. The frequencycount based association scores may then be post-processed at stage 760to reduce the noise in the data. The association score post-processingmay performed as set forth in the earlier “Post-Processing AssociationScores” section of this document.

Along the second path on the right side of FIG. 7 after stage 720, thedigital identity internet usage data is analyzed with Boolean counting.At stage 745, the internet usage data is processed with Boolean countingto calculate association scores as described in the “Boolean CountingSystem” section of this document. Next, at stage 765, the Boolean countassociation scores may then be processed with the same post-processingtechniques used to processing the frequency count association scores asset forth in the earlier “Post-Processing Association Scores” section ofthis document.

At stage 770, the processed association scores from the two differentassociation scoring systems arc then combined. Various different methodsof combining the two different association scores may be used. In oneembodiment, the frequency counting association scores and the Booleancounting association scores are combined in a weighted manner with thefollowing basic equation:

Score_(Comb)=αScore_(Freq)+βScore_(Bool)

Various different methods may be used to determine the best α and βweighting factors that are used to combine the two different associationscores. In one embodiment, class validation was used to calculate thebest α and β weighting factors. Specifically, digital identity usagedata was collected for several pairs of digital devices where each pairof devices was known to belong to a single user. Since those pairs ofdigital devices were known to be actually associated with a single user,the values of the α and β weighting factors were selected to maximizethe combined association scores those known paired devices. Again, itmust be emphasized that additional association scores may also beconsidered such that a weighted score may be created from severaldifferent association scores.

In some embodiments linear regression analysis may be used to determinehow to combine the two association scores. Specifically, with bothfrequency counting association scores and the Boolean countingassociation scores there are two different predictors. Thus, using a setof known accurate digital identity pairings, linear regression may beused to determine how to combine the two different predictors in amanner that provides accurate results.

After creating a combined association score at stage 770, the digitalpairing system may use the combined association score to selecthigh-probability digital identity pairs at stage 780. Thehigh-probability digital identity pairs may be selected from competingpotential digital identity pairs. Finally, at stage 790. the combinedprofile information from the two digital identities in a digitalidentity pair may be used to accurately select targeted advertisementsfor both digital identities in the digital identity pair.

Destination Identifiers

In the preceding examples, the common source/destination identifier usedwas a source IP address that a digital device was using to access theinternet. However, as the name implies the common source/destinationidentifier may identify a destination that is accessed by a particularclient device. An example of the destination identifier is presentedwith reference to FIGS. 2B and 8.

As illustrated in FIG. 2B household A 250 has four different digitaldevices (C_(X) 251, D_(X) 252, C_(Y) 261, and D_(Y) 262) that access theinternet 201. Since there are only four digital devices that access theinternet 201 from household A 250, various combinations of these fourdifferent digital devices may be considered potential digital identitypairings. So a set of digital device usage data associated withdestinations visited by these four different digital devices may be usedto determine high-probability digital identity pairings.

FIG. 8 illustrates a set of destination based usage observations for thefour different digital devices (C_(X) 251, D_(X) 252, C_(Y) 261, andD_(Y) 262). Specifically, FIG. 8 lists the counts of visits to thewebsites ESPN.com, CNN.com, Finance.com, and Fark.com for the fourdifferent digital devices (C_(X) 251, D_(X) 252, C_(Y) 261, and D_(Y)262). The observed visits to each web site from each of the fourdifferent digital devices all occurring within a defined time periodsuch that all the observations are current.

The same techniques disclosed in the previous sections can be used tocalculate association scores for the destination addresses. Once again,the pairing of laptop computer C_(X) 251 to one of the two cellularphones D_(X) 252 or D_(Y) 262 will be performed. To calculate anassociation score of C_(X) and D_(Y) the following information from thetable in FIG. 4 is needed:

-   -   1) C_(X) visited ESPN.com 5 times, Finance.com 2 times, fark.com        4 times    -   2) D_(Y) visited CNN.com 9 times and Finance.com 5 times    -   3) C_(X) and D_(Y) co-occurred 2 times at Finance.com    -   4) There are 56 total observations

The above observations are then used to calculateAssociation(C_(X)→D_(Y)) as follows:

P(C _(X))=# of occurrence (C _(X))/total sample size=(5+2+4))/56=11/56

P(C _(X) |D _(Y))=co-occurrences(C _(X) , D _(Y))/occurrences(D_(Y))=2/(9+5)=1/7

Association(C _(X) →D _(Y))=P(C _(X) |D _(Y))/P(C_(X))=(1/7)/(11/56)=0.73

The other potential digital identity pairing for laptop computer C_(X)251 is the pairing of C_(X) and D_(X). To calculate an association scorefor the pair of C_(X) and D_(X) the following information from the tablein FIG. 4 is needed:

-   -   1) C_(X) visited ESPN.com 5 times, Finance.com 2 times, fark.com        4 times    -   2) D_(X) visited ESPN.com 6 times, CNN.com 2 times, fark.com 6        times    -   3) C_(X) and D_(X) co-occurred 5 times at ESPN.com and 4 times        at fark.com    -   4) There are 56 total observations

The above observations are then used to calculateAssociation(C_(X)→D_(X)) as follows:

P(C _(X))=# of occurrence (C _(X))/total sample size=(5+2+4))/56=11/56

P(C _(X) |D _(X))=co-occurrences(C _(X) ,D _(X))/occurrences(D_(X))=(5+4)/(16)=9/14

Association(C _(X) →D _(X))=P(C _(X) |D _(X))/P(C_(X))=(9/14)/(11/56)=3.27

Again, the higher association score may be selected among competingdigital identity pairings. In this case, theAssociation(C_(X)→D_(X))=3.27 score is much higher than theAssociation(C_(X)→D_(Y))=0.73 score such that the pairing of laptopcomputer system C_(X) 251 and cellular phone D_(X) 252 are deemed to bea high-probability digital identity pair. Note that the BooleanAssociation score may be calculated in the same manner with regard todestination identifiers.

Matching Accuracy Measurement System

User destination information can also be used to help measure theaccuracy of digital identity pairings made with common sourceidentifiers. As set forth in earlier sections of tins document, digitalidentity pairings may be inferred by observing which digital identitiesare often seen using the same source IP address. However, inaccuratepairings may occasionally occur due to random coincidences and noise inthe data. For example, if two co-workers often go to lunch together suchthat their mobile internet devices are seen at both a workplace and alunch place together, a digital identity pairing system may mistakenlypair those two digital devices. Thus, to further verify the accuracy ofa digital identity pairing made from common source identifiers, thedestination addresses frequented by the digital identities may also beexamined.

FIG. 9A illustrates a household with two users (user X and user Y) whereeach user owns a desktop computer, a laptop computer, and a cellularsmartphone. Based upon source IP addresses, a digital pairing system hasidentified laptop computer C_(X) 951 and desktop computer T_(X) 953 as arelated pair and laptop computer C_(Y) 961 and desktop computer T_(Y)963 as a related pair. However, an analysis of the destination web sitesaccessed by those computer systems may be performed in order to ensurethe accuracy of the pairings.

Given enough user history, a digital device user's visits to destinationwebsites will show a stable pattern that can be recognized. Thus, if twopaired digital identities share similar destination website visits thenthe pairing of the digital identities is probably accurate. However, ifthe two digital identities have very different destination websitevisits then a digital identity pairing may be discarded as inaccurate.

If two different computer systems are used by the same user then thosetwo computer systems will generally not have identical web browsinghistories since the user will not visit the exact same web pages thathave already been viewed. However, the user's interests will generallybe consistent such that the user will typically access web sites in thesame general interest areas in the same proportions with both digitalidentities. Thus, if the two digital identities in a digital identitypairing visit web sites in the same general interest areas and in thesame proportions that website viewing pattern is evidence supportingthat the digital identity pairing is accurate. To quantify a digitalidentity's browsing patterns, a ‘user entropy’ value for digitalidentity may be defined as:

H _(ID)=Σ(P _(i)*log P _(i))

Where P_(i) is the percentage of accesses to interest grouping i.

The same human user browsing on different computer systems willtypically have the same level of user entropy. Thus, if two digitalidentities have very similar user entropy levels then this is evidencebacking the assertion that the same user may be using both computersystems. To compare the user entropy values for different users, an‘entropy gain’ metric may be defined as

H _(Δ) =H _(A,B)−0.5*H _(A)−0.5*H _(A)

-   -   Where H_(A,B)=H(mean(A,B))

Thus, to test if a digital pairing that has been made is accurate, theuser entropy levels for both digital identities may be calculated andthen an entropy gain may be calculated. For example, referring back toFIG. 9A, a digital pairing system has paired laptop C_(X) 951 anddesktop T_(X) 953. User entropy values may be used to test the accuracyof this pairing. FIG. 9B illustrates a list of web browsing activitieswherein laptop C_(X) 951 browsed 40% of the time on fashion websites,20% of the time on news website, 10% of the time on finance websites,and 30% of the time on other websites. Desktop T_(X) 953 browsed 35% ofthe time on fashion, 25% of the time on news, 15% of the lime on financeand 25% of the time on others. The user entropy values for laptop C_(X)951 and desktop T_(X) 953 are calculated as follows:

H_(CX)=Σ(P_(i)*log P_(i))

H _(CX)=(0.4*log 0.4+0.2*log 0.2+0.1*log 0.1+0.3*log 0.3)=6.03

H _(TX)=(0.35*log 0.35+0.25*log 0.25+0.15*log 0.15+0.25*log 0.25)=5.72

The other computer systems in the household (laptop C_(Y) 961 anddesktop T_(Y) 963) have a different browsing history and thus differentuser entropy values.

H _(CY)=(0.3*log 0.3+0.3*log 0.3+0.4*log 0.4)=3.32

H _(TY)=(0.25*log 0.25+0.35*log 0.35+0.4*log 0.4)=3.35

To calculate the entropy gain between laptop C_(X) 951 and desktop T_(X)953, first the combined entropy score H_(CX,TX) is calculated asfollows:

H _(CX,TX)=(0.375*log 0.375+0.225*log 0.225+0.125*log 0.125+0.275*log0.275)=5.84

Then the entropy gain is calculated with:

H _(Δ) =H _(CX,TX)−0.5*H _(CX)−0.5*H _(TX)

H _(Δ)=5.84−(0.5*6.03)−(0.5*5.72)=−0.035

This is a very small change in entropy thereby providing evidence thatthe digital pairing is correct. The same is true for the pairing oflaptop C_(Y) 961 and desktop T_(Y) 963 where H_(CY,TY)=3.33 and theentropy gain is

H _(Δ) =H _(CY,TY)−0.5*H _(CY)−0.5*H _(TY)

H _(Δ)=3.33−(0.5*3.32)−(0.5*3.35)=−0.01

In both the laptop C_(X) 951 and desktop T_(X) 953 pairing and thelaptop C_(Y) 961 and desktop T_(Y) 963 pairing, the entropy gains areonly slightly negative, indicating a very small decrease in useractivity diversity. In general, very small negative or positive entropygains indicate correct matching. However, if one compares the userentropy of laptop C_(X) 951 and laptop C_(Y) 961, the two systems havevery different user entropy levels. First, the combined entropy scoreH_(CX,CY) is calculated as follows:

H _(CX,CY)=(0.2*log 0.2+0.25*log 0.25+0.2*log 0.2+0.35*log 0.35)=5.65

The entropy gain value is

H _(Δ) =H _(CX,CY)−0.5*H _(CX)−0.5*H_(CY)=5.65−(0.5*6.03)−(0.5*3.32)=0.97.

This relatively large gain in entropy indicates a significant gain inthe diversity of the aggregated access pattern, thus suggesting that apairing of laptop C_(X) 951 and laptop C_(Y) 961 is incorrect.

The entropy gain metric can also be used to evaluate the accuracy ofmatching devices across different platforms. As illustrated in FIG. 9A,both user X and user Y also have cellular phones D_(X) 952 and D_(Y)962, respectively. If the cellular phones D_(X) 952 and D_(Y) 962 aresmartphones with a good web browsing ability then user entropycomparisons may be performed directly with the web browsing informationfrom the cellular phones D_(X) 952 and D_(Y) 962. However, many peopleoften do not use their cellular phones for much web browsing due to thesmall display semen and limited user interface. Thus, there may not beenough web browsing history for a useful user entropy calculation.

Instead of analyzing the web browsing history on a cellular phone, asystem may instead analyze the usage of application programs on thecellular smartphones, Apple iOS, Android, RIM Blackberry, and othercellular smartphone and tablet systems have thousands of smallapplication programs for accomplishing a wide variety of tasks. Thesedifferent application, programs can be categorized based on whatfeatures and tools the application programs provide. However, the usageof smartphone and table application programs cannot be directly comparedto web browsing histories. To handle this, a large user history of webbrowsing and smartphone/tablet application program usage has beenanalyzed. There is a correlation between smartphone/table applicationprogram usage and web browsing patterns such that one can infer a likelyweb browsing pattern from a known smartphone/tablet application programusage history. The inferred web browsing pattern can then be used inuser entropy comparisons.

For example, the user of cellular phone D_(X) 952 is a light mobileapplication user that has the following application program usagepattern: 30% on casual games, 30% on life style, 20% on news, 10% onfinance, and 10% on other application programs. The user of cellularphone D_(Y) 962 has the following application program usage pattern: 50%on action games, 20% on news, 20% on finance and 10% on other apps. Witha large amount of historical information web browsing and applicationusage on known users, a system for inferring web browsing patternsapplication usage patterns. For example, from the application usagepattern of cellular phone D_(X) 952 an inference system may determinethat such a user may allocate their web browsing as follows: 20% onfashion, 25% on news, 15% on finance websites, and 40% on others. In thesame manner, the user of cellular phone D_(Y) 962 may web browse 25% onnews, 20% on finance, 20% on games and 35% on others.

From these inferred web browsing patterns, the user entropy metrics maybe calculated. The inferred user entropy values would be:

H _(DIX)=(0.20*log 0.20+0.25*log 0.25+0.15*log 0.15+0.4*log 0.4)=5.81

H _(DIY)=(0.25*log 0.25+0.20*log 0.20+0.20*log 0.20+0.35*log 0.35)=5.65

Assuming that laptop C_(X) 951 and desktop T_(X) 953 were correctlypaired then one can compare the inferred web browsing history ofcellular phone D_(X) 952 with the group laptop C_(X) 951 and desktopT_(X) 953.

H _(DIX,CX,TX)=(0.2875*log 0.2875+0.2375*log 0.2375+0.1375*log0.1375+0.3375*log 0.3375)=5.75

The entropy gain may then be calculated as follows:

$\begin{matrix}{H_{\Delta} = {H_{{DIX},{CX},{TX}} - {0.5*H_{DIX}} - {0.5*H_{{CX},{TX}}}}} \\{= {{5.75 - \left( {0.5*5.81} \right) - \left( {0.5*5.84} \right)} = {- 0.07}}}\end{matrix}$

Such a relatively small entropy gain would provide evidence supportingthat the devices are used by the same user. If one attempts to matchcellular phone D_(X) 952 with laptop C_(Y) 961 and desktop T_(Y) 963then the entropy gain is 1.42(H_(Δ)=H_(DIX,CY,TY)−0.5*H_(DIX)−0.5*H_(CY,TY)=1.42). Note that the webbrowsing inference process may not always be accurate such that higherentropy gains may appear. Thus, inference data may be better used forhelping select from a set of possible matches. For example, the entropygain of matching cellular phone D_(Y) 962 with laptop C_(X) 951 anddesktop T_(X) 953 is 2.64 whereas the entropy gain of matching cellularphone D_(Y) 962 with laptop C_(Y) 961 and desktop T_(Y) 963 is 1.46 suchthat it would be better to match cellular phone D_(Y) 962 with laptopC_(Y) 961 and desktop T_(Y) 963 Instead of with laptop C_(X) 951 anddesktop T_(X) 953. Thus, even with an imperfect inferred website accesspattern (which is quite different from an actual recorded websitepattern) the system can still determine that cellular phone D_(X) 952matches laptop C_(X) 951 and desktop T_(X) 953 better than cellularphone D_(Y) 962; while cellular phone D_(Y) 962 matches laptop C_(Y) 961and desktop T_(Y) 963 better than cellular phone D_(X) 952.

Occasionally, this system may encounter two different users have similarwebsite access patterns, and the entropy gain metric will erroneouslyconsider digital identities from the two different users to be from thesame user. However, even when this mismatch occurs this mismatch willnot decrease the power of targeting advertisement because these twousers have the same interests.

User Session Level Sampling

In the previous sections, the source/destination identifier was the maindistinguishing factor used to identify high-probability pairs. However,the timestamp can also be used to help identify digital identity pairs.Specifically, different digital devices that are often used at the samelocation around the same time may have a higher probability of beingrelated to the same user. Thus, some embodiments of a digital pairingsystem use the timestamps in the triads of observed usage data to helpcalculate association scores.

FIG. 10A illustrate a log of usage triads 1010 wherein a clientidentifier of User1 has been observed visiting a series of destinationidentifiers (URL1 to URL9) wherein each observation has an associatedtimestamp (T0 to T9). To determine if different digital identities areused around the same time, the concept of a user “session” may be used.A session is defined as a set usage observations that are never morethan a session threshold amount of time between successive observations.If more than a session threshold amount of time passes between twoconsecutive digital identity observations then those two digitalidentity observations are deemed to be from two separate user sessions.

Various different methods may be used to calculate the session thresholdamount of time. In one embodiment, an analysis of time gaps betweendigital identity usage observations is performed to calculate a sessionthreshold amount of time. Referring to FIG. 10A, the amount of timebetween each digital identity observation timestamp (T0 to T9) iscalculated as a gap time (g1 to g9) 1050. Once a large collection of gaptimes has been calculated, these gap times are then analyzed withstandard normal distribution statistics analysis. Initially, all of theobserved gap times arc placed into gap time buckets as illustrated inFIG. 11. Then, the mean (μ) and standard deviation (σ) for the gap timesare calculated as illustrated in FIG. 11. Anything that is less than 2standard deviations (σ) from the mean is considered to be within thesame session. Thus, all of the gap times 1110 are deemed to be withinthe same user session. Note that this is just one possible embodimentand that many other methods may be used to determine what constitutes auser session. User sessions may be determined using other criteria suchthat user session time periods may vary in length.

After determining a method of splitting observations into temporallydistinct sessions, the session data may be processed with the sameassociation score methodology set forth in the previous sections. Forexample, FIG. 10B illustrates a set of sessions for the laptop computersC_(X) 251 and C_(Y) 261 in household A 250 of FIG. 2B. For each session,the usage observations of the four different digital devices (C_(X) 251,D_(X) 252, C_(Y) 261, and D_(Y) 262) are listed. Since the differentusers tend to use their laptop computers and cellular phones atdifferent times, the two digital pairings of the two different devicesmay be distinguished despite only having observations from a singlelocation (household A 250).

The techniques disclosed in the previous sections may be used tocalculate association scores for the four different sessions depicted inFIG. 10B. Once again, the pairing of laptop computer C_(X) 251 to one ofthe two cellular phones D_(X) 252 or D_(Y) 262 in household A 250 willbe performed. To calculate an association score of C_(X) and D_(Y) thefollowing information from the table in FIG. 10B is needed:

-   -   1) C_(X) observations: 4 in session 1, 1 in session 3, 14 in        session 4    -   2) D_(Y) observations: 12 in session 2, 7 in session 3, 1 in        session 4    -   3) C_(X) and D_(Y) co-occurred once in session 3 and once in        session 4    -   4) There are 74 total observations

The above Internet usage observations arc then used to calculateAssociation (C_(X)→D_(Y)) as follows:

P(C _(X)=# occurrence (C _(X))/total sample size=(4+1+14))/74=19/74

P(C _(X) |D _(Y))=co-occurrences(C _(X) , D _(Y))/occurrences(D_(Y))=2/(12+7+1)=1/10

Association(C _(X) →D _(Y))−P(C _(X) |D _(Y))=P(C_(X))−(1/10)/(19/74)−0.39

The other potential digital identity pairing for laptop computer C_(X)251 is the pairing of C_(X) and D_(X). To calculate an association scorefor the pair of C_(X) and D_(X) following information from the table inFIG. 10B is needed:

-   -   1) C_(X) observations: 4 in session 1, 1 in session 3, 14 in        session 4    -   2) D_(X) observations: 2 in session 1, 9 in session 4    -   3) C_(X) and D_(X) co-occurred twice in session1 and 9 times in        session 4    -   4) There are 74 total observations

The above observations are then used to calculateAssociation(C_(X)→D_(X)) as follows:

P(C _(X))=# of occurrence (C _(X))/total sample size=(5+2+4))/56=11/56

P(C _(X) |D _(X))=co-occurrences(C _(X) ,D _(X))/occurrences(D_(X))=(2+9)/(11)=1

Association(C _(X) →D _(X))=P(C _(X) |D _(X))/P(C _(X))=1/(11/56)=5.09

With data depicted in FIG. 10B, the Association(C_(X)→D_(X))=5.09 scoreis much higher than the Association(C_(X)→D_(Y))=0.39 score such thatthe pairing of laptop computer system C_(X) 251 and cellular phone D_(X)252 are deemed to be a high-probability digital identity pair. Note thatthe Boolean Association score may be calculated in the same manner forthese session-generated association scores.

Digital Identity Chaining

In the preceding sections of this document various techniques have beendisclosed for identifying digital identities that are likely to belongto the same human user. When such digital identity pairs are determined,the advertising to both digital identities can be synergisticallyimproved by combining digital profile information collected on bothdigital identities. However, the process does not have to end withsimple pairings of digital identities. The technique can be extended tocombine multiple digital identities together thereby further improvingthe accuracy of advertising targeting. The technique of combiningtogether the digital profiles collected from multiple digital identitiesmay be referred to as ‘digital identity chaining’.

Referring back to FIG. 2A, household A 250 included laptop computers 251and 261, cellular telephones 252 and 262, tablet computer system 257,and video game console 259. All of these digital electronic devices arecoupled to the internet 201 and capable of receiving and displayinginternet advertisements. In the preceding sections of this document,techniques disclosed how the usage patterns of the laptop computers 251and 261 and the cellular telephones 252 and 262 could be used toidentify laptop computer 251 and cellular telephone 252 as belonging toa single user X. User X may proceed to install an application ontocellular telephone 252 that is linked to a user account on video gameconsole 259. Thus, user X may self-identify cellular telephone 252 andvideo game console 259 as a pair of linked identities. Since cellulartelephone 252 was already linked to laptop computer 251 that means thatlaptop computer 251 is linked to video game console 259 by thetransitive property such that digital profile information from laptopcomputer 251 may be used to selected targeted advertisements to videogame console 259.

Digital identity chaining can synergistically improve the targeting ofinternet advertisements. For example, the accumulated informationcollected from laptop computer 251, cellular telephone 252, and videogame console 259 may be used to create a very detailed digital profileof user X. This detailed digital profile of user X may then he usedwhenever an internet advertisement must be selected for laptop computer251, cellular telephone 252, or video game console 259.

Note that in the above example, the cellular telephone 252 and videogame console 259 were linked together using a specific applicationprogram installed onto cellular telephone 252. However, the two devicescould have been pair together using the association score systemsdescribed in earlier sections. Thus, digital identity chaining may usemany different methods of linking together different digital devices.

The preceding technical disclosure is intended to he illustrative, andnot restrictive. For examples, the above-described embodiments (or oneor more aspects thereof) may be used in combination with each other.Other embodiments will be apparent to those of skill in the art uponreviewing the above description. The scope of the claims should,therefore, be determined with reference to the appended claims, alongwith the full scope of equivalents to which such claims are entitled. Inthe appended claims, the terms “including” and “in which” are used asthe plain-English equivalents of the respective terms “comprising” and“wherein.” Also, in the following claims, the terms “including” and“comprising” are open-ended, that is, a system, device, article, orprocess that includes elements in addition to those listed after such aterm in a claim is still deemed to fall within the scope of that claim.Moreover, in the following claims, the terms “first,” “second,” and“third,” etc. are used merely as labels, and are not intended to imposenumerical requirements on their objects.

The Abstract is provided to comply with 37 C.F.R. §1.72(b), whichrequires that it allow the reader to quickly ascertain the nature of thetechnical disclosure. The abstract is submitted with the understandingthat it will not he used to interpret or limit the scope or meaning ofthe claims. Also, in the above Detailed Description, various featuresmay be grouped together to streamline the disclosure. This should not beinterpreted as intending that an unclaimed disclosed feature isessential to any claim. Rather, inventive subject matter may lie in lessthan all features of a particular disclosed embodiment. Thus, thefollowing claims are hereby incorporated into the Detailed Description,with each claim standing on its own as a separate embodiment.

1. (canceled)
 2. A method of pairing internet-connected devices basedupon server connections, said method comprising: collecting over theinternet, using one or more internet web site servers, internet web siteusage observations from internet-connected devices, each observationincluding an internet-connected device identity that identifies aninternet-connected device, a server source/destination identifier, eachindividual observation indicating an occurrence of a connection betweenan internet-connected device associated with an internet-connecteddevice identity within the observation and a server associated with aserver source/destination identifier within the observation; storing thecollected observations in at least one server log in at least onenon-transitory storage device; and determining, using at least oneserver system, multiple individual internet-connected device pairings,each individual internet-connected device pairing determined based atleast in part upon a frequency of co-occurrences of stored observations,wherein for each co-occurrence of observations, one observation of theco-occurrence of observations includes one internet-connected deviceidentity that identifies one internet-connected device of the individualinternet-connected device pairing, another observation of theco-occurrence of observations includes another internet-connected deviceidentity that identifies another internet-connected device of theindividual internet-connected device pairing, and the one observation ofthe co-occurrence of observations includes a server source/destinationidentifier that matches a server source/destination identifier includedwithin the other observation of the co-occurrence of observations. 3.The method of claim 2 further including: selecting, for at least oneinternet-connected device pairing, information to send to oneinternet-connected device associated with the at least oneinternet-connected device pairing based at least in part on profileinformation associated with another internet-connected device associatedwith the at least one internet-connected device pairing.
 4. The methodof claim 2 further including: selecting, for at least oneinternet-connected device pairing, information to send to oneinternet-connected device associated with the at least oneinternet-connected device pairing based at least in part on profileinformation associated with another internet-connected device associatedwith the at least one internet-connected device pairing; and sending theselected to the information over the internet to the one identifiedinternet-connected device.
 5. The method of claim 2, wherein eachobservation further includes a timestamp that indicates a time ofoccurrence of a connection between an internet-connected deviceassociated with an internet-connected device identity within theobservation and a server associated with a server source/destinationidentifier within the observation; and wherein for each co-occurrence ofobservations, the one observation of the co-occurrence of observationsincludes a time stamp within at least one time window, and the otherobservation of the co-occurrence of observations includes a time stampwithin the at least one time window.
 6. The method of claim 2, whereindetermining multiple individual internet-connected device pairingsfurther includes: identifying multiple potential internet-connecteddevice pairings based at least in part upon the observations;calculating individual association scores based at least in part uponthe frequency of co-occurrences of observations, each individualassociation score representing a likelihood that a correspondingindividual potential internet-connected device pairing correctlyidentifies a corresponding pair of internet-connected device that areassociated with the same user; and selecting internet-connected devicepairings based at least in part upon the association scores.
 7. Themethod of claim 6, wherein said selecting internet-connected devicepairings based upon the association scores further includes: determiningwhether an association score corresponding to an internet-connecteddevice identity meets a threshold.
 8. The method of claim 6, whereincalculating individual association scores includes calculating supportscores corresponding to potential internet-connected device pairings ofthe identified set using said observations; wherein each calculatedsupport score provides an indication of how much support there is for ananalysis of its corresponding potential internet-connected device pair;and wherein selecting potential internet-connected device pair includesusing support scores to filter out observations that lack a prescribedamount of support.
 9. The method of claim 6, wherein calculatingindividual association scores includes calculating confidence scorescorresponding to potential internet-connected device pair of theidentified set using said observations; wherein each calculatedconfidence score provides an indication of how much confidence there isfor a corresponding association score; and wherein selecting potentialinternet-connected device pair includes using confidence scores tofilter out internet-connected device pair.
 10. The method of claim 6,wherein calculating individual association scores includes calculatingsupport scores corresponding to potential internet-connected devicepairings of the identified set using said observations; wherein eachcalculated support score provides an indication of how much supportthere is for an analysis of its corresponding potentialinternet-connected device pair; wherein selecting potentialinternet-connected device pair includes using support scores to filterout observations that lack a prescribed amount of support; whereincalculating individual association scores includes calculatingconfidence scores corresponding to potential internet-connected devicepair of the identified set using said observations; wherein eachcalculated confidence score provides an indication of how muchconfidence there is for a corresponding association score; and whereinselecting potential internet-connected device pair includes usingconfidence scores to filter out internet-connected device pair.
 11. Themethod of claim 2, wherein said selecting internet-connected devicepairings based upon the association scores includes: for each of aplurality of different given internet-connected devices included withinmore than one potential internet-connected device pairing, identifying apotential internet-connected device pairing including the giveninternet-connected device that corresponds to an association score thatindicates a greater likelihood of being associated with the same userthan does a different association score associated with a differentpotential internet-connected device pairing including the giveninternet-connected device ; and determining whether the associationscore that corresponds to the identified potential internet-connecteddevice meets a threshold.
 12. The method of claim 6 wherein calculatingassociation scores for said potential internet-connected device pairingsare calculated with Bayesian inference.
 13. The method of claim 2wherein said internet-connected device identities comprise web browsercookies.
 14. The method of claim 2 wherein said serversource/destination identifiers comprise Internet Protocol addresses. 15.The method of claim 2, wherein said internet-connected device identitiesinclude cellular phone device identifiers.
 16. The method of claim 2,wherein said internet-connected device identities include web browsercookies and cellular phone device identifiers.
 17. The method of claim 3further including: providing in a computer readable storage devicecombined profiles corresponding to the internet-connected device pairs,wherein each combined profile indicates profile information collectedfor each device associated with its corresponding selectedinternet-connected device pair.
 18. The method of claim 17, whereinproviding combined profiles corresponding to internet-connected devicepairs includes, for each of multiple internet-connected device pairs,providing in a computer readable storage device a combined profile forat least one internet-connected device of a correspondinginternet-connected device pair based on web browsing history of a devicecorresponding to the at least one digital identity.
 19. The method ofclaim 17, wherein providing combined profiles corresponding tointernet-connected device pairs includes, for each of multipleinternet-connected device pairs, providing in a computer readablestorage device a combined profile for at least one internet-connecteddevice of a corresponding internet-connected device pair based on basedon search terms entered into search engines using a device correspondingto the at least one internet-connected device.
 20. The method of claim17, wherein providing combined profiles corresponding tointernet-connected device pairs includes, for each of multipleinternet-connected device pairs, providing in a computer readablestorage device a combined profile for at least one internet-connecteddevice of a corresponding internet-connected device pair based oncontent of web pages visited using a device corresponding to the atleast one internet-connected device.
 21. The method of claim 17, whereinproviding combined profiles corresponding to internet-connected devicepairs, includes for each of multiple internet-connected device pairs,providing in a computer readable storage device a combined profile forat least one internet-connected device of a correspondinginternet-connected device pair based on data input to a registrationsystem using a device corresponding to the at least oneinternet-connected device.
 22. The method of claim 17, wherein providingcombined profiles corresponding to internet-connected device pairs,includes for each of multiple internet-connected device pairs, combiningaccumulated profile data for two separate digital identities to providea profile of a human user associated with the paired digital identities.23. The method of claim 17, wherein providing combined profilescorresponding to internet-connected device pairs, includes for each ofmultiple internet-connected device pairs, accumulating information foreach device associated with the internet-connected device pair.
 24. Themethod of claim 3, wherein selecting includes, selecting based at leastin part upon web browsing history included within the profileinformation from the another respective internet-connected deviceassociated with the same selected internet-connected device pairing asthe selected respective internet-connected device.
 25. The method ofclaim 3, wherein selecting includes, selecting based at least in partupon search terms entered into search engines included within theprofile information from another respective internet-connected devicehaving an internet-connected device included within the same selectedinternet-connected device pair as the selected respectiveinternet-connected device.
 26. The method of claim 3, wherein selectingincludes, selecting based at least in part upon content of web pagesvisited included within the profile information from another respectiveinternet-connected device having an internet-connected device includedwithin the same selected internet-connected device pairing as theselected respective internet-connected device.
 27. The method of claim3, wherein selecting includes, selecting based at least in part upondata input to a registration system included within the profileinformation from another respective internet-connected device having aninternet-connected device included within the same selectedinternet-connected device pairing as the selected respectiveinternet-connected device.
 28. The method of claim 3, wherein selectingincludes, selecting based at least in part upon combined accumulatedprofile data included within the profile information from the selectedrespective internet-connected device and profile information includedwithin another respective internet-connected device having aninternet-connected device included within the same selectedinternet-connected device pairing as the selected respectiveinternet-connected device.
 29. The method of claim 2, whereindetermining includes, matching stored observations that include bothsame internet-connected device identities and same serversource/destination identifiers; and storing in at least onenon-transitory storage device counts of matching collected observationsthat include both same internet-connected device identities and sameserver source/destination identifiers.
 30. The method of claim 5,wherein determining includes, matching stored observations that includeboth same internet-connected device identities and same serversource/destination identifiers and at least one timestamp that indicatesa time of occurrence of a connection within at least one time window;and storing in at least one non-transitory storage device counts ofmatching collected observations that include both sameinternet-connected device identities and same server source/destinationidentifiers and an indication of at least one timestamp that indicates atime of occurrence of a connection within at least one time window.